Abstract: An insertion and retraction device is described for delivery and removal of a microparticle from target tissue. The device comprises a magnetic or magnetizable needle or cannula having a distal end, a proximal end and a lumen adapted to convey microparticles; a tubular catheter receiving the needle or cannula; a pressure device adapted for delivery of microparticles through the needle or cannula lumen by pressure; a magnetic field modulator adapted to move the needle or cannula by modulation of a magnetic field; and a magnetic sensor positioned toward the distal end of the tubular catheter responsive to a magnetic moment of the microparticle.

Abstract: Apparatus and methods for imaging and tracking of nano- and micro-scale objects with acceptable latency for relevant medical procedures, such as delivery of therapeutic payload or minimally invasive surgery are disclosed, including the capability to superimpose accurate anatomical data over a tracking image. Software applications are provided for data logging via a remote-control station; and software interface with remote motion control mechanism, controlling the motion of internal device.

Abstract: There is provided a method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion; computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from a plurality of CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing the predictive fact

Abstract: There is provided a method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion; computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from a plurality of CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing the predictive fact

Abstract: There is provided a method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion; computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from a plurality of CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing the predictive fact

Abstract: There is provided a method for predicting risk of osteoporotic fracture, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, the CT scan being performed with settings selected for imaging of non-osteoporosis related pathology; processing the imaging data to identify the bone portion; automatically extracting features based on the imaging data denoting the identified bone portion; computing an osteoporotic fracture predictive factor indicative of the risk of developing at least one osteoporotic fracture in the patient, or the risk of the patient having at least one severe osteoporotic fracture, based on the extracted features, the predictive factor calculated by applying a trained osteoporotic fracture classifier to the extracted features, the osteoporotic fracture classifier trained from data from a plurality of CT scans performed with settings selected for imaging non-osteoporosis related pathology; and providing the predictive fact

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data , computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data , computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data, computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data , computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: Computerized methods and systems for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data by receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion, segmenting the bone portion from the imaging data, computing at least one grade based on pixel associated values from the bone portion, and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan. The grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.

Abstract: There is provided a computer-implemented method for detecting a fatty liver, comprising: receiving imaging data of a computed tomography (CT) scan performed using a single source CT Scanner with settings selected for imaging of non-fatty-liver pathology, segmenting a region of the liver by creating a binary image by applying binary segmentation to a sub-set of pixels of the imaging data according to a first set-of-rules, and mapping the region of liver of the binary image to the segmented region of the portion of the liver of the imaging data, calculating liver parameter(s) for the segmented region of the liver from Hounsfield unit (HU) value(s), and detecting the presence of a fatty liver by analyzing the calculated liver parameter(s) according to a second set-of-rules.

Abstract: There is provided a computer-implemented method for detecting a fatty liver, comprising: receiving imaging data of a computed tomography (CT) scan performed using a single source CT Scanner with settings selected for imaging of non-fatty-liver pathology, segmenting a region of the liver by creating a binary image by applying binary segmentation to a sub-set of pixels of the imaging data according to a first set-of-rules, and mapping the region of liver of the binary image to the segmented region of the portion of the liver of the imaging data, calculating liver parameter(s) for the segmented region of the liver from Hounsfield unit (HU) value(s), and detecting the presence of a fatty liver by analyzing the calculated liver parameter(s) according to a second set-of-rules.

Abstract: There is provided a computerized method for estimating a dual-energy X-ray absorptiometry (DEXA) score from CT imaging data, comprising: receiving imaging data of a computed tomography (CT) scan of a body of a patient containing at least a bone portion; segmenting the bone portion from the imaging data; computing at least one grade based on pixel associated values from the bone portion; and correlating the at least one grade with at least one score representing a relation to bone density values in a population obtained based on a DEXA scan; wherein the grade is computed from a calculation of sub-grades performed for each one or a set of pixels having at least one of a common medial-lateral axial coordinate and a common cranial-caudal axial coordinate along a dorsal-ventral axis of a volume representation of the imaging data.